Overview

Dataset statistics

Number of variables19
Number of observations50000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.2 MiB
Average record size in memory152.0 B

Variable types

Numeric11
Text4
Categorical4

Alerts

acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
time_signature is highly imbalanced (63.2%)Imbalance
instance_id is uniformly distributedUniform
genre is uniformly distributedUniform
popularity has 2195 (4.4%) zerosZeros
instrumentalness has 17416 (34.8%) zerosZeros

Reproduction

Analysis started2023-09-10 05:29:07.833757
Analysis finished2023-09-10 05:29:31.176785
Duration23.34 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

instance_id
Real number (ℝ)

UNIFORM 

Distinct5000
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2500.5
Minimum1
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:31.316606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile250.95
Q11250.75
median2500.5
Q33750.25
95-th percentile4750.05
Maximum5000
Range4999
Interquartile range (IQR)2499.5

Descriptive statistics

Standard deviation1443.3901
Coefficient of variation (CV)0.57724058
Kurtosis-1.2000001
Mean2500.5
Median Absolute Deviation (MAD)1250
Skewness0
Sum1.25025 × 108
Variance2083374.9
MonotonicityNot monotonic
2023-09-10T17:29:31.512434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 10
 
< 0.1%
3331 10
 
< 0.1%
3338 10
 
< 0.1%
3337 10
 
< 0.1%
3336 10
 
< 0.1%
3335 10
 
< 0.1%
3334 10
 
< 0.1%
3333 10
 
< 0.1%
3332 10
 
< 0.1%
3330 10
 
< 0.1%
Other values (4990) 49900
99.8%
ValueCountFrequency (%)
1 10
< 0.1%
2 10
< 0.1%
3 10
< 0.1%
4 10
< 0.1%
5 10
< 0.1%
6 10
< 0.1%
7 10
< 0.1%
8 10
< 0.1%
9 10
< 0.1%
10 10
< 0.1%
ValueCountFrequency (%)
5000 10
< 0.1%
4999 10
< 0.1%
4998 10
< 0.1%
4997 10
< 0.1%
4996 10
< 0.1%
4995 10
< 0.1%
4994 10
< 0.1%
4993 10
< 0.1%
4992 10
< 0.1%
4991 10
< 0.1%
Distinct5679
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:31.965538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length84
Median length51
Mean length11.8007
Min length1

Characters and Unicode

Total characters590035
Distinct characters119
Distinct categories15 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1799 ?
Unique (%)3.6%

Sample

1st rowChromeo
2nd rowSango
3rd rowCounting Crows
4th rowBullet For My Valentine
5th rowShinedown
ValueCountFrequency (%)
empty_field 10065
 
11.0%
the 4654
 
5.1%
851
 
0.9%
music 741
 
0.8%
black 409
 
0.4%
of 398
 
0.4%
randy 377
 
0.4%
newman 362
 
0.4%
children's 335
 
0.4%
and 325
 
0.4%
Other values (6770) 73058
79.8%
2023-09-10T17:29:32.631461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 64280
 
10.9%
41575
 
7.0%
i 37492
 
6.4%
a 34484
 
5.8%
l 30338
 
5.1%
n 28313
 
4.8%
t 28028
 
4.8%
o 27769
 
4.7%
r 25318
 
4.3%
d 22079
 
3.7%
Other values (109) 250359
42.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 445244
75.5%
Uppercase Letter 87747
 
14.9%
Space Separator 41575
 
7.0%
Connector Punctuation 10065
 
1.7%
Other Punctuation 3427
 
0.6%
Decimal Number 1143
 
0.2%
Dash Punctuation 458
 
0.1%
Currency Symbol 252
 
< 0.1%
Math Symbol 62
 
< 0.1%
Open Punctuation 23
 
< 0.1%
Other values (5) 39
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 64280
14.4%
i 37492
 
8.4%
a 34484
 
7.7%
l 30338
 
6.8%
n 28313
 
6.4%
t 28028
 
6.3%
o 27769
 
6.2%
r 25318
 
5.7%
d 22079
 
5.0%
s 20553
 
4.6%
Other values (37) 126590
28.4%
Uppercase Letter
ValueCountFrequency (%)
T 8160
 
9.3%
S 7314
 
8.3%
B 6962
 
7.9%
M 6627
 
7.6%
C 6420
 
7.3%
A 4997
 
5.7%
D 4718
 
5.4%
J 4277
 
4.9%
L 4229
 
4.8%
R 4194
 
4.8%
Other values (24) 29849
34.0%
Other Punctuation
ValueCountFrequency (%)
. 1331
38.8%
' 776
22.6%
& 739
21.6%
! 251
 
7.3%
" 144
 
4.2%
, 144
 
4.2%
/ 17
 
0.5%
: 13
 
0.4%
? 5
 
0.1%
* 4
 
0.1%
Other values (2) 3
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 242
21.2%
2 174
15.2%
5 112
9.8%
0 112
9.8%
3 108
9.4%
7 101
8.8%
9 96
 
8.4%
8 81
 
7.1%
4 63
 
5.5%
6 54
 
4.7%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 455
99.3%
3
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 16
69.6%
[ 7
30.4%
Close Punctuation
ValueCountFrequency (%)
) 16
69.6%
] 7
30.4%
Space Separator
ValueCountFrequency (%)
41575
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10065
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 252
100.0%
Math Symbol
ValueCountFrequency (%)
+ 62
100.0%
Final Punctuation
ValueCountFrequency (%)
9
100.0%
Other Symbol
ValueCountFrequency (%)
3
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 532989
90.3%
Common 57043
 
9.7%
Han 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 64280
 
12.1%
i 37492
 
7.0%
a 34484
 
6.5%
l 30338
 
5.7%
n 28313
 
5.3%
t 28028
 
5.3%
o 27769
 
5.2%
r 25318
 
4.8%
d 22079
 
4.1%
s 20553
 
3.9%
Other values (70) 214335
40.2%
Common
ValueCountFrequency (%)
41575
72.9%
_ 10065
 
17.6%
. 1331
 
2.3%
' 776
 
1.4%
& 739
 
1.3%
- 455
 
0.8%
$ 252
 
0.4%
! 251
 
0.4%
1 242
 
0.4%
2 174
 
0.3%
Other values (26) 1183
 
2.1%
Han
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 589121
99.8%
None 893
 
0.2%
Punctuation 15
 
< 0.1%
Dingbats 3
 
< 0.1%
CJK 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 64280
 
10.9%
41575
 
7.1%
i 37492
 
6.4%
a 34484
 
5.9%
l 30338
 
5.1%
n 28313
 
4.8%
t 28028
 
4.8%
o 27769
 
4.7%
r 25318
 
4.3%
d 22079
 
3.7%
Other values (71) 249445
42.3%
None
ValueCountFrequency (%)
é 456
51.1%
ö 126
 
14.1%
á 52
 
5.8%
è 31
 
3.5%
ø 27
 
3.0%
ñ 23
 
2.6%
í 23
 
2.6%
ü 21
 
2.4%
Ö 17
 
1.9%
É 17
 
1.9%
Other values (20) 100
 
11.2%
Punctuation
ValueCountFrequency (%)
9
60.0%
3
 
20.0%
2
 
13.3%
1
 
6.7%
Dingbats
ValueCountFrequency (%)
3
100.0%
CJK
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Distinct42472
Distinct (%)84.9%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:32.992626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length273
Median length174
Mean length18.27628
Min length1

Characters and Unicode

Total characters913814
Distinct characters251
Distinct categories20 ?
Distinct scripts6 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37651 ?
Unique (%)75.3%

Sample

1st rowCome Alive (feat. Toro Y Moi)
2nd rowHow Do You Love Me
3rd rowOmaha
4th rowOver It
5th rowAsking For It
ValueCountFrequency (%)
7285
 
4.2%
the 6472
 
3.8%
you 2453
 
1.4%
a 2122
 
1.2%
i 2000
 
1.2%
feat 1952
 
1.1%
of 1881
 
1.1%
me 1728
 
1.0%
in 1672
 
1.0%
to 1658
 
1.0%
Other values (23265) 142877
83.0%
2023-09-10T17:29:33.571987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
122101
 
13.4%
e 85529
 
9.4%
o 54942
 
6.0%
a 51823
 
5.7%
i 45658
 
5.0%
n 45628
 
5.0%
t 42447
 
4.6%
r 40649
 
4.4%
s 31180
 
3.4%
l 28937
 
3.2%
Other values (241) 364920
39.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 593833
65.0%
Uppercase Letter 156429
 
17.1%
Space Separator 122101
 
13.4%
Other Punctuation 16896
 
1.8%
Decimal Number 8411
 
0.9%
Dash Punctuation 6413
 
0.7%
Open Punctuation 4621
 
0.5%
Close Punctuation 4616
 
0.5%
Final Punctuation 188
 
< 0.1%
Currency Symbol 116
 
< 0.1%
Other values (10) 190
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 85529
14.4%
o 54942
 
9.3%
a 51823
 
8.7%
i 45658
 
7.7%
n 45628
 
7.7%
t 42447
 
7.1%
r 40649
 
6.8%
s 31180
 
5.3%
l 28937
 
4.9%
h 22991
 
3.9%
Other values (74) 144049
24.3%
Uppercase Letter
ValueCountFrequency (%)
T 13573
 
8.7%
S 13316
 
8.5%
M 10996
 
7.0%
L 9930
 
6.3%
B 9460
 
6.0%
A 9087
 
5.8%
I 8102
 
5.2%
C 7703
 
4.9%
D 7672
 
4.9%
W 7518
 
4.8%
Other values (44) 59072
37.8%
Other Letter
ValueCountFrequency (%)
4
 
7.0%
3
 
5.3%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
Other values (33) 34
59.6%
Other Punctuation
ValueCountFrequency (%)
' 5099
30.2%
. 4017
23.8%
, 2220
13.1%
/ 1649
 
9.8%
" 1338
 
7.9%
& 940
 
5.6%
! 456
 
2.7%
: 453
 
2.7%
? 412
 
2.4%
* 143
 
0.8%
Other values (10) 169
 
1.0%
Decimal Number
ValueCountFrequency (%)
0 1811
21.5%
2 1619
19.2%
1 1603
19.1%
9 783
9.3%
4 512
 
6.1%
5 457
 
5.4%
3 441
 
5.2%
7 435
 
5.2%
8 405
 
4.8%
6 345
 
4.1%
Math Symbol
ValueCountFrequency (%)
+ 28
49.1%
| 16
28.1%
4
 
7.0%
= 3
 
5.3%
< 2
 
3.5%
2
 
3.5%
> 1
 
1.8%
~ 1
 
1.8%
Other Symbol
ValueCountFrequency (%)
5
33.3%
4
26.7%
2
 
13.3%
2
 
13.3%
° 1
 
6.7%
® 1
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 6401
99.8%
9
 
0.1%
3
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 4429
95.8%
[ 191
 
4.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 4424
95.8%
] 191
 
4.1%
1
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
171
91.0%
14
 
7.4%
» 3
 
1.6%
Initial Punctuation
ValueCountFrequency (%)
14
73.7%
« 3
 
15.8%
2
 
10.5%
Currency Symbol
ValueCountFrequency (%)
$ 115
99.1%
¥ 1
 
0.9%
Modifier Symbol
ValueCountFrequency (%)
` 4
57.1%
´ 3
42.9%
Other Number
ValueCountFrequency (%)
² 2
66.7%
½ 1
33.3%
Space Separator
ValueCountFrequency (%)
122101
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 23
100.0%
Format
ValueCountFrequency (%)
6
100.0%
Control
ValueCountFrequency (%)
’ 2
100.0%
Modifier Letter
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 750127
82.1%
Common 163495
 
17.9%
Cyrillic 135
 
< 0.1%
Han 23
 
< 0.1%
Katakana 18
 
< 0.1%
Hiragana 16
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 85529
 
11.4%
o 54942
 
7.3%
a 51823
 
6.9%
i 45658
 
6.1%
n 45628
 
6.1%
t 42447
 
5.7%
r 40649
 
5.4%
s 31180
 
4.2%
l 28937
 
3.9%
h 22991
 
3.1%
Other values (93) 300343
40.0%
Common
ValueCountFrequency (%)
122101
74.7%
- 6401
 
3.9%
' 5099
 
3.1%
( 4429
 
2.7%
) 4424
 
2.7%
. 4017
 
2.5%
, 2220
 
1.4%
0 1811
 
1.1%
/ 1649
 
1.0%
2 1619
 
1.0%
Other values (60) 9725
 
5.9%
Cyrillic
ValueCountFrequency (%)
о 12
 
8.9%
и 12
 
8.9%
а 10
 
7.4%
н 10
 
7.4%
е 8
 
5.9%
т 8
 
5.9%
ы 7
 
5.2%
я 5
 
3.7%
р 5
 
3.7%
к 5
 
3.7%
Other values (25) 53
39.3%
Han
ValueCountFrequency (%)
2
 
8.7%
2
 
8.7%
2
 
8.7%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
Other values (10) 10
43.5%
Katakana
ValueCountFrequency (%)
4
22.2%
3
16.7%
2
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other values (2) 2
11.1%
Hiragana
ValueCountFrequency (%)
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
1
6.2%
1
6.2%
1
6.2%
1
6.2%
1
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 912115
99.8%
None 1245
 
0.1%
Punctuation 239
 
< 0.1%
Cyrillic 135
 
< 0.1%
CJK 23
 
< 0.1%
Katakana 21
 
< 0.1%
Hiragana 16
 
< 0.1%
Geometric Shapes 11
 
< 0.1%
Math Operators 6
 
< 0.1%
Misc Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
122101
 
13.4%
e 85529
 
9.4%
o 54942
 
6.0%
a 51823
 
5.7%
i 45658
 
5.0%
n 45628
 
5.0%
t 42447
 
4.7%
r 40649
 
4.5%
s 31180
 
3.4%
l 28937
 
3.2%
Other values (82) 363221
39.8%
None
ValueCountFrequency (%)
é 445
35.7%
ó 120
 
9.6%
è 98
 
7.9%
á 85
 
6.8%
í 70
 
5.6%
à 67
 
5.4%
ñ 66
 
5.3%
ê 37
 
3.0%
ç 31
 
2.5%
ú 16
 
1.3%
Other values (53) 210
16.9%
Punctuation
ValueCountFrequency (%)
171
71.5%
16
 
6.7%
14
 
5.9%
14
 
5.9%
9
 
3.8%
6
 
2.5%
4
 
1.7%
3
 
1.3%
2
 
0.8%
Cyrillic
ValueCountFrequency (%)
о 12
 
8.9%
и 12
 
8.9%
а 10
 
7.4%
н 10
 
7.4%
е 8
 
5.9%
т 8
 
5.9%
ы 7
 
5.2%
я 5
 
3.7%
р 5
 
3.7%
к 5
 
3.7%
Other values (25) 53
39.3%
Geometric Shapes
ValueCountFrequency (%)
5
45.5%
4
36.4%
2
 
18.2%
Math Operators
ValueCountFrequency (%)
4
66.7%
2
33.3%
Katakana
ValueCountFrequency (%)
4
19.0%
3
14.3%
2
9.5%
2
9.5%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (4) 4
19.0%
Hiragana
ValueCountFrequency (%)
2
12.5%
2
12.5%
2
12.5%
2
12.5%
2
12.5%
1
6.2%
1
6.2%
1
6.2%
1
6.2%
1
6.2%
CJK
ValueCountFrequency (%)
2
 
8.7%
2
 
8.7%
2
 
8.7%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
Other values (10) 10
43.5%
Misc Symbols
ValueCountFrequency (%)
2
100.0%
IPA Ext
ValueCountFrequency (%)
ʇ 1
100.0%
Distinct46378
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:33.860142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters1100000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43114 ?
Unique (%)86.2%

Sample

1st row0ZulY8etRzj54NLYOYX7jy
2nd row1jgkBLmR16qqZzEG5LYZN5
3rd row50o13VmOJiUj3WfU7XmEAn
4th row5BRGRuHRfvGgoIvZg3PE3x
5th row0BpgI6YcYv3ENGLp7XJsAH
ValueCountFrequency (%)
6svqnuvcvftxvlk3ec0ngd 5
 
< 0.1%
0hrd6csafhhqkptyfppmqh 5
 
< 0.1%
0hovoz4m0ajp6vy4fvri51 5
 
< 0.1%
1vzlew5zfcabkz94xqiszf 5
 
< 0.1%
6aite2iej1qklaofpjczw1 5
 
< 0.1%
7mzpvdn4oekmucjmcba6do 4
 
< 0.1%
7jjh8f3phlnvxfqeaaffdl 4
 
< 0.1%
3dh10qbwbwvqamjnnhlrq4 4
 
< 0.1%
359krpyckcff8sfvqwes9l 4
 
< 0.1%
65xy6cx0263j5bpny8mpye 4
 
< 0.1%
Other values (46368) 49955
99.9%
2023-09-10T17:29:34.290145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 23585
 
2.1%
1 23558
 
2.1%
0 23450
 
2.1%
5 23357
 
2.1%
6 23297
 
2.1%
2 23291
 
2.1%
4 23242
 
2.1%
7 22037
 
2.0%
B 17210
 
1.6%
y 17151
 
1.6%
Other values (52) 879822
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 440492
40.0%
Uppercase Letter 439466
40.0%
Decimal Number 220042
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 17210
 
3.9%
X 17075
 
3.9%
I 17064
 
3.9%
L 17048
 
3.9%
O 17043
 
3.9%
Z 17042
 
3.9%
E 17033
 
3.9%
R 17027
 
3.9%
M 17016
 
3.9%
P 17014
 
3.9%
Other values (16) 268894
61.2%
Lowercase Letter
ValueCountFrequency (%)
y 17151
 
3.9%
t 17131
 
3.9%
l 17101
 
3.9%
h 17094
 
3.9%
e 17064
 
3.9%
w 17019
 
3.9%
n 17004
 
3.9%
c 17004
 
3.9%
a 16990
 
3.9%
s 16986
 
3.9%
Other values (16) 269948
61.3%
Decimal Number
ValueCountFrequency (%)
3 23585
10.7%
1 23558
10.7%
0 23450
10.7%
5 23357
10.6%
6 23297
10.6%
2 23291
10.6%
4 23242
10.6%
7 22037
10.0%
9 17115
7.8%
8 17110
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 879958
80.0%
Common 220042
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 17210
 
2.0%
y 17151
 
1.9%
t 17131
 
1.9%
l 17101
 
1.9%
h 17094
 
1.9%
X 17075
 
1.9%
e 17064
 
1.9%
I 17064
 
1.9%
L 17048
 
1.9%
O 17043
 
1.9%
Other values (42) 708977
80.6%
Common
ValueCountFrequency (%)
3 23585
10.7%
1 23558
10.7%
0 23450
10.7%
5 23357
10.6%
6 23297
10.6%
2 23291
10.6%
4 23242
10.6%
7 22037
10.0%
9 17115
7.8%
8 17110
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 23585
 
2.1%
1 23558
 
2.1%
0 23450
 
2.1%
5 23357
 
2.1%
6 23297
 
2.1%
2 23291
 
2.1%
4 23242
 
2.1%
7 22037
 
2.0%
B 17210
 
1.6%
y 17151
 
1.6%
Other values (52) 879822
80.0%

popularity
Real number (ℝ)

ZEROS 

Distinct94
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.6808
Minimum0
Maximum96
Zeros2195
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:34.634858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q125
median41
Q351
95-th percentile64
Maximum96
Range96
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.205972
Coefficient of variation (CV)0.4831631
Kurtosis-0.5643404
Mean37.6808
Median Absolute Deviation (MAD)12
Skewness-0.38039972
Sum1884040
Variance331.45742
MonotonicityNot monotonic
2023-09-10T17:29:34.845880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2195
 
4.4%
49 1333
 
2.7%
50 1291
 
2.6%
48 1288
 
2.6%
47 1249
 
2.5%
53 1203
 
2.4%
51 1188
 
2.4%
44 1171
 
2.3%
46 1157
 
2.3%
43 1156
 
2.3%
Other values (84) 36769
73.5%
ValueCountFrequency (%)
0 2195
4.4%
1 561
 
1.1%
2 353
 
0.7%
3 280
 
0.6%
4 202
 
0.4%
5 189
 
0.4%
6 157
 
0.3%
7 141
 
0.3%
8 163
 
0.3%
9 177
 
0.4%
ValueCountFrequency (%)
96 2
 
< 0.1%
93 2
 
< 0.1%
91 1
 
< 0.1%
90 3
< 0.1%
89 2
 
< 0.1%
88 1
 
< 0.1%
87 3
< 0.1%
86 3
< 0.1%
85 4
< 0.1%
84 6
< 0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct4194
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35095266
Minimum1.28 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:35.053202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.28 × 10-6
5-th percentile0.00039
Q10.0249
median0.219
Q30.699
95-th percentile0.93
Maximum0.996
Range0.99599872
Interquartile range (IQR)0.6741

Descriptive statistics

Standard deviation0.34280612
Coefficient of variation (CV)0.97678734
Kurtosis-1.3146508
Mean0.35095266
Median Absolute Deviation (MAD)0.21575
Skewness0.51006988
Sum17547.633
Variance0.11751603
MonotonicityNot monotonic
2023-09-10T17:29:35.257359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 76
 
0.2%
0.105 70
 
0.1%
0.123 69
 
0.1%
0.829 67
 
0.1%
0.109 67
 
0.1%
0.819 66
 
0.1%
0.104 65
 
0.1%
0.113 65
 
0.1%
0.102 65
 
0.1%
0.119 65
 
0.1%
Other values (4184) 49325
98.7%
ValueCountFrequency (%)
1.28 × 10-61
< 0.1%
1.3 × 10-61
< 0.1%
1.39 × 10-61
< 0.1%
1.42 × 10-61
< 0.1%
1.55 × 10-61
< 0.1%
1.57 × 10-61
< 0.1%
1.72 × 10-61
< 0.1%
2.13 × 10-61
< 0.1%
2.24 × 10-61
< 0.1%
2.27 × 10-61
< 0.1%
ValueCountFrequency (%)
0.996 20
 
< 0.1%
0.995 76
0.2%
0.994 61
0.1%
0.993 49
0.1%
0.992 43
0.1%
0.991 43
0.1%
0.99 35
0.1%
0.989 35
0.1%
0.988 50
0.1%
0.987 46
0.1%

danceability
Real number (ℝ)

Distinct915
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57250592
Minimum0.0617
Maximum0.989
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:35.459539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0617
5-th percentile0.297
Q10.467
median0.576
Q30.685
95-th percentile0.83
Maximum0.989
Range0.9273
Interquartile range (IQR)0.218

Descriptive statistics

Standard deviation0.15941893
Coefficient of variation (CV)0.27845813
Kurtosis-0.30280715
Mean0.57250592
Median Absolute Deviation (MAD)0.109
Skewness-0.15853586
Sum28625.296
Variance0.025414394
MonotonicityNot monotonic
2023-09-10T17:29:35.656586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.62 150
 
0.3%
0.578 146
 
0.3%
0.595 142
 
0.3%
0.576 141
 
0.3%
0.529 140
 
0.3%
0.597 139
 
0.3%
0.539 139
 
0.3%
0.545 138
 
0.3%
0.623 138
 
0.3%
0.574 137
 
0.3%
Other values (905) 48590
97.2%
ValueCountFrequency (%)
0.0617 2
< 0.1%
0.0658 1
< 0.1%
0.0662 1
< 0.1%
0.0665 1
< 0.1%
0.071 1
< 0.1%
0.0734 1
< 0.1%
0.0759 1
< 0.1%
0.0768 1
< 0.1%
0.0809 1
< 0.1%
0.0812 1
< 0.1%
ValueCountFrequency (%)
0.989 1
 
< 0.1%
0.986 1
 
< 0.1%
0.985 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 1
 
< 0.1%
0.98 4
< 0.1%
0.979 1
 
< 0.1%
0.978 2
< 0.1%
0.977 2
< 0.1%
0.976 2
< 0.1%

duration_ms
Real number (ℝ)

Distinct25159
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181711.41
Minimum-1
Maximum4830606
Zeros0
Zeros (%)0.0%
Negative10022
Negative (%)20.0%
Memory size390.8 KiB
2023-09-10T17:29:35.856564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1107849.75
median195308.5
Q3246936.25
95-th percentile359075
Maximum4830606
Range4830607
Interquartile range (IQR)139086.5

Descriptive statistics

Standard deviation145614.41
Coefficient of variation (CV)0.80134985
Kurtosis141.10384
Mean181711.41
Median Absolute Deviation (MAD)61691.5
Skewness6.5731073
Sum9.0855703 × 109
Variance2.1203556 × 1010
MonotonicityNot monotonic
2023-09-10T17:29:36.065628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 10022
 
20.0%
240000 31
 
0.1%
192000 25
 
0.1%
180000 22
 
< 0.1%
186000 19
 
< 0.1%
216000 19
 
< 0.1%
194000 15
 
< 0.1%
211200 14
 
< 0.1%
198400 14
 
< 0.1%
264000 13
 
< 0.1%
Other values (25149) 39806
79.6%
ValueCountFrequency (%)
-1 10022
20.0%
18000 1
 
< 0.1%
18080 1
 
< 0.1%
18173 1
 
< 0.1%
19040 1
 
< 0.1%
19959 1
 
< 0.1%
20213 1
 
< 0.1%
21053 1
 
< 0.1%
21133 1
 
< 0.1%
21160 1
 
< 0.1%
ValueCountFrequency (%)
4830606 1
< 0.1%
4791725 1
< 0.1%
3920687 1
< 0.1%
3832947 1
< 0.1%
3769399 1
< 0.1%
3475594 1
< 0.1%
3473453 1
< 0.1%
3452316 1
< 0.1%
3435625 1
< 0.1%
3331765 1
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct1511
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61875308
Minimum0.000216
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:36.268631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.000216
5-th percentile0.18095
Q10.445
median0.641
Q30.825
95-th percentile0.962
Maximum0.999
Range0.998784
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.2423242
Coefficient of variation (CV)0.39163313
Kurtosis-0.74856118
Mean0.61875308
Median Absolute Deviation (MAD)0.189
Skewness-0.3774966
Sum30937.654
Variance0.05872102
MonotonicityNot monotonic
2023-09-10T17:29:36.438625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.721 105
 
0.2%
0.72 103
 
0.2%
0.714 100
 
0.2%
0.959 100
 
0.2%
0.967 97
 
0.2%
0.592 95
 
0.2%
0.859 95
 
0.2%
0.943 93
 
0.2%
0.979 93
 
0.2%
0.843 93
 
0.2%
Other values (1501) 49026
98.1%
ValueCountFrequency (%)
0.000216 1
< 0.1%
0.000499 1
< 0.1%
0.00141 1
< 0.1%
0.00202 1
< 0.1%
0.00264 1
< 0.1%
0.0029 1
< 0.1%
0.00319 1
< 0.1%
0.00441 1
< 0.1%
0.00491 1
< 0.1%
0.00562 1
< 0.1%
ValueCountFrequency (%)
0.999 5
 
< 0.1%
0.998 18
 
< 0.1%
0.997 21
 
< 0.1%
0.996 46
0.1%
0.995 66
0.1%
0.994 52
0.1%
0.993 53
0.1%
0.992 53
0.1%
0.991 53
0.1%
0.99 71
0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct5163
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09337794
Minimum0
Maximum0.993
Zeros17416
Zeros (%)34.8%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:36.601628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.29 × 10-5
Q30.0106
95-th percentile0.78605
Maximum0.993
Range0.993
Interquartile range (IQR)0.0106

Descriptive statistics

Standard deviation0.2318472
Coefficient of variation (CV)2.4828905
Kurtosis5.3540463
Mean0.09337794
Median Absolute Deviation (MAD)3.29 × 10-5
Skewness2.5875121
Sum4668.897
Variance0.053753122
MonotonicityNot monotonic
2023-09-10T17:29:36.757670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17416
34.8%
0.000106 36
 
0.1%
0.00108 32
 
0.1%
1.13 × 10-530
 
0.1%
1.09 × 10-529
 
0.1%
0.11 29
 
0.1%
0.868 29
 
0.1%
1.12 × 10-628
 
0.1%
1.07 × 10-528
 
0.1%
0.000122 27
 
0.1%
Other values (5153) 32316
64.6%
ValueCountFrequency (%)
0 17416
34.8%
1 × 10-69
 
< 0.1%
1.01 × 10-624
 
< 0.1%
1.02 × 10-619
 
< 0.1%
1.03 × 10-616
 
< 0.1%
1.04 × 10-621
 
< 0.1%
1.05 × 10-624
 
< 0.1%
1.06 × 10-614
 
< 0.1%
1.07 × 10-623
 
< 0.1%
1.08 × 10-67
 
< 0.1%
ValueCountFrequency (%)
0.993 1
< 0.1%
0.992 1
< 0.1%
0.991 2
< 0.1%
0.988 1
< 0.1%
0.986 1
< 0.1%
0.984 1
< 0.1%
0.982 1
< 0.1%
0.981 1
< 0.1%
0.98 1
< 0.1%
0.979 1
< 0.1%

key
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
C
5921 
G
5687 
D
5180 
C#
5105 
A
4939 
Other values (7)
23168 

Length

Max length2
Median length1
Mean length1.32826
Min length1

Characters and Unicode

Total characters66413
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowA#
3rd rowA
4th rowB
5th rowA#

Common Values

ValueCountFrequency (%)
C 5921
11.8%
G 5687
11.4%
D 5180
10.4%
C# 5105
10.2%
A 4939
9.9%
F 4274
8.5%
B 3904
7.8%
E 3682
7.4%
F# 3413
6.8%
A# 3220
6.4%
Other values (2) 4675
9.3%

Length

2023-09-10T17:29:36.893625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c 11026
22.1%
g 8824
17.6%
a 8159
16.3%
f 7687
15.4%
d 6718
13.4%
b 3904
 
7.8%
e 3682
 
7.4%

Most occurring characters

ValueCountFrequency (%)
# 16413
24.7%
C 11026
16.6%
G 8824
13.3%
A 8159
12.3%
F 7687
11.6%
D 6718
10.1%
B 3904
 
5.9%
E 3682
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 50000
75.3%
Other Punctuation 16413
 
24.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 11026
22.1%
G 8824
17.6%
A 8159
16.3%
F 7687
15.4%
D 6718
13.4%
B 3904
 
7.8%
E 3682
 
7.4%
Other Punctuation
ValueCountFrequency (%)
# 16413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50000
75.3%
Common 16413
 
24.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 11026
22.1%
G 8824
17.6%
A 8159
16.3%
F 7687
15.4%
D 6718
13.4%
B 3904
 
7.8%
E 3682
 
7.4%
Common
ValueCountFrequency (%)
# 16413
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
# 16413
24.7%
C 11026
16.6%
G 8824
13.3%
A 8159
12.3%
F 7687
11.6%
D 6718
10.1%
B 3904
 
5.9%
E 3682
 
5.5%

liveness
Real number (ℝ)

Distinct1673
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25699562
Minimum0.0124
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:37.031629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0124
5-th percentile0.0639
Q10.101
median0.144
Q30.326
95-th percentile0.845
Maximum1
Range0.9876
Interquartile range (IQR)0.225

Descriptive statistics

Standard deviation0.23881833
Coefficient of variation (CV)0.92927004
Kurtosis1.4874045
Mean0.25699562
Median Absolute Deviation (MAD)0.0627
Skewness1.5953836
Sum12849.781
Variance0.057034194
MonotonicityNot monotonic
2023-09-10T17:29:37.178363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11 507
 
1.0%
0.111 502
 
1.0%
0.108 476
 
1.0%
0.109 475
 
0.9%
0.112 463
 
0.9%
0.105 461
 
0.9%
0.106 450
 
0.9%
0.107 448
 
0.9%
0.104 417
 
0.8%
0.102 412
 
0.8%
Other values (1663) 45389
90.8%
ValueCountFrequency (%)
0.0124 1
 
< 0.1%
0.0138 1
 
< 0.1%
0.0145 1
 
< 0.1%
0.0146 1
 
< 0.1%
0.015 1
 
< 0.1%
0.0152 1
 
< 0.1%
0.0166 1
 
< 0.1%
0.0189 1
 
< 0.1%
0.0194 1
 
< 0.1%
0.0196 3
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.999 1
 
< 0.1%
0.997 1
 
< 0.1%
0.996 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.993 4
< 0.1%
0.992 2
 
< 0.1%
0.991 3
< 0.1%
0.99 6
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct15600
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.7366433
Minimum-38.445
Maximum3.744
Zeros1
Zeros (%)< 0.1%
Negative49974
Negative (%)99.9%
Memory size390.8 KiB
2023-09-10T17:29:37.322167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-38.445
5-th percentile-17.096
Q1-11.04
median-7.82
Q3-5.562
95-th percentile-3.34795
Maximum3.744
Range42.189
Interquartile range (IQR)5.478

Descriptive statistics

Standard deviation4.3963511
Coefficient of variation (CV)-0.50320826
Kurtosis2.0716946
Mean-8.7366433
Median Absolute Deviation (MAD)2.597
Skewness-1.1930941
Sum-436832.17
Variance19.327903
MonotonicityNot monotonic
2023-09-10T17:29:37.457149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-8.582 17
 
< 0.1%
-6.196 16
 
< 0.1%
-7.722 15
 
< 0.1%
-6.031 15
 
< 0.1%
-7.241 15
 
< 0.1%
-5.135 15
 
< 0.1%
-5.439 15
 
< 0.1%
-4.099 14
 
< 0.1%
-8.296 14
 
< 0.1%
-7.508 14
 
< 0.1%
Other values (15590) 49850
99.7%
ValueCountFrequency (%)
-38.445 1
< 0.1%
-37.336 1
< 0.1%
-36.582 1
< 0.1%
-35.456 1
< 0.1%
-35.233 1
< 0.1%
-34.993 1
< 0.1%
-34.308 1
< 0.1%
-34.113 1
< 0.1%
-33.909 1
< 0.1%
-33.141 1
< 0.1%
ValueCountFrequency (%)
3.744 1
< 0.1%
1.949 1
< 0.1%
1.314 1
< 0.1%
1.275 1
< 0.1%
1.258 1
< 0.1%
1.012 1
< 0.1%
0.948 1
< 0.1%
0.878 1
< 0.1%
0.754 1
< 0.1%
0.732 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
Major
33487 
Minor
16513 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters250000
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMinor
2nd rowMinor
3rd rowMajor
4th rowMinor
5th rowMajor

Common Values

ValueCountFrequency (%)
Major 33487
67.0%
Minor 16513
33.0%

Length

2023-09-10T17:29:37.583182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-10T17:29:37.708195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
major 33487
67.0%
minor 16513
33.0%

Most occurring characters

ValueCountFrequency (%)
M 50000
20.0%
o 50000
20.0%
r 50000
20.0%
a 33487
13.4%
j 33487
13.4%
i 16513
 
6.6%
n 16513
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 200000
80.0%
Uppercase Letter 50000
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 50000
25.0%
r 50000
25.0%
a 33487
16.7%
j 33487
16.7%
i 16513
 
8.3%
n 16513
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
M 50000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 250000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 50000
20.0%
o 50000
20.0%
r 50000
20.0%
a 33487
13.4%
j 33487
13.4%
i 16513
 
6.6%
n 16513
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 50000
20.0%
o 50000
20.0%
r 50000
20.0%
a 33487
13.4%
j 33487
13.4%
i 16513
 
6.6%
n 16513
 
6.6%

speechiness
Real number (ℝ)

Distinct1611
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17285543
Minimum0.0222
Maximum0.965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:37.832191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0222
5-th percentile0.0287
Q10.0374
median0.0576
Q30.153
95-th percentile0.928
Maximum0.965
Range0.9428
Interquartile range (IQR)0.1156

Descriptive statistics

Standard deviation0.25863223
Coefficient of variation (CV)1.4962344
Kurtosis3.6294252
Mean0.17285543
Median Absolute Deviation (MAD)0.0255
Skewness2.2294183
Sum8642.7713
Variance0.06689063
MonotonicityNot monotonic
2023-09-10T17:29:37.982200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0328 142
 
0.3%
0.0304 141
 
0.3%
0.0343 139
 
0.3%
0.0332 138
 
0.3%
0.0336 138
 
0.3%
0.0329 137
 
0.3%
0.0339 133
 
0.3%
0.033 133
 
0.3%
0.0305 133
 
0.3%
0.0362 132
 
0.3%
Other values (1601) 48634
97.3%
ValueCountFrequency (%)
0.0222 1
 
< 0.1%
0.0226 1
 
< 0.1%
0.0228 3
 
< 0.1%
0.0229 2
 
< 0.1%
0.023 1
 
< 0.1%
0.0231 9
< 0.1%
0.0232 11
< 0.1%
0.0233 4
 
< 0.1%
0.0234 5
< 0.1%
0.0235 3
 
< 0.1%
ValueCountFrequency (%)
0.965 8
 
< 0.1%
0.964 6
 
< 0.1%
0.963 9
 
< 0.1%
0.962 28
0.1%
0.961 20
< 0.1%
0.96 35
0.1%
0.959 31
0.1%
0.958 33
0.1%
0.957 45
0.1%
0.956 47
0.1%

tempo
Text

Distinct30461
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:38.375192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.73338
Min length1

Characters and Unicode

Total characters286669
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22891 ?
Unique (%)45.8%

Sample

1st row129.97
2nd row129.992
3rd row80.883
4th row97.625
5th row144.031
ValueCountFrequency (%)
7461
 
14.9%
120.006 16
 
< 0.1%
100.008 14
 
< 0.1%
120.016 14
 
< 0.1%
120.011 12
 
< 0.1%
119.993 12
 
< 0.1%
120.01 12
 
< 0.1%
150.007 12
 
< 0.1%
119.994 12
 
< 0.1%
100.003 11
 
< 0.1%
Other values (30451) 42424
84.8%
2023-09-10T17:29:38.787193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 49605
17.3%
. 42365
14.8%
9 28713
10.0%
0 24395
8.5%
8 21184
7.4%
2 20677
7.2%
7 19641
 
6.9%
3 18735
 
6.5%
4 18682
 
6.5%
5 18031
 
6.3%
Other values (2) 24641
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 236843
82.6%
Other Punctuation 49826
 
17.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 49605
20.9%
9 28713
12.1%
0 24395
10.3%
8 21184
8.9%
2 20677
8.7%
7 19641
 
8.3%
3 18735
 
7.9%
4 18682
 
7.9%
5 18031
 
7.6%
6 17180
 
7.3%
Other Punctuation
ValueCountFrequency (%)
. 42365
85.0%
? 7461
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Common 286669
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 49605
17.3%
. 42365
14.8%
9 28713
10.0%
0 24395
8.5%
8 21184
7.4%
2 20677
7.2%
7 19641
 
6.9%
3 18735
 
6.5%
4 18682
 
6.5%
5 18031
 
6.3%
Other values (2) 24641
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 286669
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 49605
17.3%
. 42365
14.8%
9 28713
10.0%
0 24395
8.5%
8 21184
7.4%
2 20677
7.2%
7 19641
 
6.9%
3 18735
 
6.5%
4 18682
 
6.5%
5 18031
 
6.3%
Other values (2) 24641
8.6%

time_signature
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
4-Apr
42927 
3-Apr
5293 
5-Apr
 
1156
1-Apr
 
624

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters250000
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4-Apr
2nd row4-Apr
3rd row4-Apr
4th row4-Apr
5th row4-Apr

Common Values

ValueCountFrequency (%)
4-Apr 42927
85.9%
3-Apr 5293
 
10.6%
5-Apr 1156
 
2.3%
1-Apr 624
 
1.2%

Length

2023-09-10T17:29:38.936146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-10T17:29:39.055167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4-apr 42927
85.9%
3-apr 5293
 
10.6%
5-apr 1156
 
2.3%
1-apr 624
 
1.2%

Most occurring characters

ValueCountFrequency (%)
- 50000
20.0%
A 50000
20.0%
p 50000
20.0%
r 50000
20.0%
4 42927
17.2%
3 5293
 
2.1%
5 1156
 
0.5%
1 624
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100000
40.0%
Dash Punctuation 50000
20.0%
Uppercase Letter 50000
20.0%
Decimal Number 50000
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 42927
85.9%
3 5293
 
10.6%
5 1156
 
2.3%
1 624
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
p 50000
50.0%
r 50000
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 50000
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 50000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 150000
60.0%
Common 100000
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 50000
50.0%
4 42927
42.9%
3 5293
 
5.3%
5 1156
 
1.2%
1 624
 
0.6%
Latin
ValueCountFrequency (%)
A 50000
33.3%
p 50000
33.3%
r 50000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 250000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 50000
20.0%
A 50000
20.0%
p 50000
20.0%
r 50000
20.0%
4 42927
17.2%
3 5293
 
2.1%
5 1156
 
0.5%
1 624
 
0.2%

valence
Real number (ℝ)

Distinct1567
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48576394
Minimum0
Maximum1
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size390.8 KiB
2023-09-10T17:29:39.181153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.101
Q10.289
median0.477
Q30.676
95-th percentile0.91
Maximum1
Range1
Interquartile range (IQR)0.387

Descriptive statistics

Standard deviation0.24713651
Coefficient of variation (CV)0.50875845
Kurtosis-0.93235137
Mean0.48576394
Median Absolute Deviation (MAD)0.193
Skewness0.13353805
Sum24288.197
Variance0.061076456
MonotonicityNot monotonic
2023-09-10T17:29:39.329185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 137
 
0.3%
0.964 101
 
0.2%
0.962 100
 
0.2%
0.963 100
 
0.2%
0.377 94
 
0.2%
0.556 91
 
0.2%
0.358 90
 
0.2%
0.549 88
 
0.2%
0.398 88
 
0.2%
0.38 87
 
0.2%
Other values (1557) 49024
98.0%
ValueCountFrequency (%)
0 12
< 0.1%
0.0241 1
 
< 0.1%
0.025 1
 
< 0.1%
0.0267 1
 
< 0.1%
0.0275 1
 
< 0.1%
0.0276 1
 
< 0.1%
0.0277 1
 
< 0.1%
0.0284 1
 
< 0.1%
0.0287 1
 
< 0.1%
0.0291 1
 
< 0.1%
ValueCountFrequency (%)
1 4
< 0.1%
0.999 1
 
< 0.1%
0.998 1
 
< 0.1%
0.996 1
 
< 0.1%
0.992 1
 
< 0.1%
0.991 2
< 0.1%
0.99 2
< 0.1%
0.989 4
< 0.1%
0.987 4
< 0.1%
0.986 1
 
< 0.1%

genre
Categorical

UNIFORM 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size390.8 KiB
Alternative
5000 
Blues
5000 
Children's Music
5000 
Comedy
5000 
Electronic
5000 
Other values (5)
25000 

Length

Max length16
Median length8.5
Mean length7.1
Min length3

Characters and Unicode

Total characters355000
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlternative
2nd rowAlternative
3rd rowAlternative
4th rowAlternative
5th rowAlternative

Common Values

ValueCountFrequency (%)
Alternative 5000
10.0%
Blues 5000
10.0%
Children's Music 5000
10.0%
Comedy 5000
10.0%
Electronic 5000
10.0%
Folk 5000
10.0%
Hip-Hop 5000
10.0%
Movie 5000
10.0%
Ska 5000
10.0%
Soul 5000
10.0%

Length

2023-09-10T17:29:39.470151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-10T17:29:39.626149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
alternative 5000
9.1%
blues 5000
9.1%
children's 5000
9.1%
music 5000
9.1%
comedy 5000
9.1%
electronic 5000
9.1%
folk 5000
9.1%
hip-hop 5000
9.1%
movie 5000
9.1%
ska 5000
9.1%

Most occurring characters

ValueCountFrequency (%)
e 35000
 
9.9%
i 30000
 
8.5%
o 30000
 
8.5%
l 30000
 
8.5%
t 15000
 
4.2%
r 15000
 
4.2%
n 15000
 
4.2%
u 15000
 
4.2%
s 15000
 
4.2%
c 15000
 
4.2%
Other values (19) 140000
39.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 280000
78.9%
Uppercase Letter 60000
 
16.9%
Space Separator 5000
 
1.4%
Other Punctuation 5000
 
1.4%
Dash Punctuation 5000
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 35000
12.5%
i 30000
10.7%
o 30000
10.7%
l 30000
10.7%
t 15000
 
5.4%
r 15000
 
5.4%
n 15000
 
5.4%
u 15000
 
5.4%
s 15000
 
5.4%
c 15000
 
5.4%
Other values (8) 65000
23.2%
Uppercase Letter
ValueCountFrequency (%)
M 10000
16.7%
H 10000
16.7%
S 10000
16.7%
C 10000
16.7%
E 5000
8.3%
F 5000
8.3%
B 5000
8.3%
A 5000
8.3%
Space Separator
ValueCountFrequency (%)
5000
100.0%
Other Punctuation
ValueCountFrequency (%)
' 5000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 340000
95.8%
Common 15000
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 35000
 
10.3%
i 30000
 
8.8%
o 30000
 
8.8%
l 30000
 
8.8%
t 15000
 
4.4%
r 15000
 
4.4%
n 15000
 
4.4%
u 15000
 
4.4%
s 15000
 
4.4%
c 15000
 
4.4%
Other values (16) 125000
36.8%
Common
ValueCountFrequency (%)
5000
33.3%
' 5000
33.3%
- 5000
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 355000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 35000
 
9.9%
i 30000
 
8.5%
o 30000
 
8.5%
l 30000
 
8.5%
t 15000
 
4.2%
r 15000
 
4.2%
n 15000
 
4.2%
u 15000
 
4.2%
s 15000
 
4.2%
c 15000
 
4.2%
Other values (19) 140000
39.4%

Interactions

2023-09-10T17:29:28.972777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:11.871766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:13.474174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:15.051426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:17.121822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:19.177510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:21.422396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:22.955072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:24.466977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:25.946545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:27.395869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:29.110267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:12.026769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:13.616713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:15.208805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:17.304821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:19.384470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:21.560395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:23.091548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:24.601979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:26.077550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:27.643905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:29.248132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:12.175798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:13.754659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:15.376100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:17.486822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:19.584551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:21.698432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:23.225065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:24.734231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:26.209080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:27.778879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:29.381159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:12.323401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:13.914786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:15.555559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:17.686494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:19.914899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:21.838397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:23.363021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:24.877268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:26.339083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:27.911906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:29.507056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:12.462832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:14.051571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:15.755484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:17.883490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:20.096837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:21.979448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:23.491418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:25.014235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:26.467870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:28.045867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:29.636056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:12.608585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:14.196525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:15.968794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:18.110124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:20.289902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:22.122428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:23.629305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:25.144229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:26.601873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:28.183027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:29.765102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:12.756582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:14.335531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:16.143805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:18.292488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:20.477820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:22.261739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:23.766256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:25.283545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:26.743871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:28.314490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:29.895056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:12.906991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:14.479564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:16.355738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:18.478342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:20.690171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:22.406046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:23.901306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:25.420547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:26.880871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:28.448768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:30.027079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:13.048980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:14.620700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:16.534959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:18.655434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:20.888547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:22.545110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:24.040298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:25.552594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:27.013029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:28.585769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:30.148059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:13.190478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:14.764694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:16.732002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:18.826429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:21.105557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:22.681070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:24.178262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:25.682552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:27.138012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:28.711767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:30.282788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:13.328478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:14.910846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:16.933822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:18.992447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:21.278987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:22.820067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:24.322259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:25.814542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:27.268300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-10T17:29:28.841803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-10T17:29:39.781151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
instance_idpopularityacousticnessdanceabilityduration_msenergyinstrumentalnesslivenessloudnessspeechinessvalencekeymodetime_signaturegenre
instance_id1.0000.002-0.0040.009-0.005-0.0030.001-0.0060.003-0.0020.0080.0050.0080.0030.000
popularity0.0021.000-0.3040.1400.1330.0830.026-0.1970.268-0.118-0.0830.0380.1100.1390.384
acousticness-0.004-0.3041.000-0.015-0.092-0.617-0.1620.126-0.6240.066-0.1160.0420.0920.2060.254
danceability0.0090.140-0.0151.000-0.043-0.089-0.103-0.1420.0120.1460.3560.0330.0710.1070.169
duration_ms-0.0050.133-0.092-0.0431.0000.0340.106-0.0320.031-0.080-0.1170.0080.0060.0230.043
energy-0.0030.083-0.617-0.0890.0341.000-0.0260.1790.7480.2480.1830.0410.0910.1100.207
instrumentalness0.0010.026-0.162-0.1030.106-0.0261.000-0.199-0.074-0.338-0.1180.0150.0350.0280.137
liveness-0.006-0.1970.126-0.142-0.0320.179-0.1991.000-0.0160.315-0.0450.0330.0160.1640.237
loudness0.0030.268-0.6240.0120.0310.748-0.074-0.0161.0000.0410.1430.0260.0670.1390.175
speechiness-0.002-0.1180.0660.146-0.0800.248-0.3380.3150.0411.0000.0200.0610.0610.2250.341
valence0.008-0.083-0.1160.356-0.1170.183-0.118-0.0450.1430.0201.0000.0230.0510.0840.117
key0.0050.0380.0420.0330.0080.0410.0150.0330.0260.0610.0231.0000.2680.0410.085
mode0.0080.1100.0920.0710.0060.0910.0350.0160.0670.0610.0510.2681.0000.0330.184
time_signature0.0030.1390.2060.1070.0230.1100.0280.1640.1390.2250.0840.0410.0331.0000.235
genre0.0000.3840.2540.1690.0430.2070.1370.2370.1750.3410.1170.0850.1840.2351.000

Missing values

2023-09-10T17:29:30.497792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-10T17:29:30.874794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

instance_idartist_nametrack_nametrack_idpopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalencegenre
01ChromeoCome Alive (feat. Toro Y Moi)0ZulY8etRzj54NLYOYX7jy430.2250000.8452386800.7460.000088F0.0785-5.655Minor0.0383129.974-Apr0.961Alternative
12SangoHow Do You Love Me1jgkBLmR16qqZzEG5LYZN5470.6650000.8621661540.3420.000082A#0.1020-10.095Minor0.0551129.9924-Apr0.177Alternative
23Counting CrowsOmaha50o13VmOJiUj3WfU7XmEAn480.2380000.5902194000.5170.000000A0.1820-9.239Major0.031280.8834-Apr0.566Alternative
34Bullet For My ValentineOver It5BRGRuHRfvGgoIvZg3PE3x600.0000770.374-10.9710.000000B0.3220-4.284Minor0.069797.6254-Apr0.701Alternative
45ShinedownAsking For It0BpgI6YcYv3ENGLp7XJsAH580.0027300.449-10.9710.000000A#0.1410-3.660Major0.0476144.0314-Apr0.414Alternative
56HalestormDo Not Disturb13PqmoiHO8rzblmaNFHY4g530.0140000.696-10.8210.000000D0.0421-2.981Minor0.0483127.0064-Apr0.604Alternative
67empty_fieldJump the Gun5bUVHuzQh5mkvMPjUU074i420.0150000.596-10.9250.018000A0.3800-3.381Major0.0731166.7594-Apr0.798Alternative
78Sixx:A.M.Rise4FXHLmzOMGLy5jFj6ctDsn480.0016000.323-10.9790.001920A#0.3490-2.820Major0.1280178.1264-Apr0.294Alternative
89Electric GuestOh Devil - Radio Version3PYtlkObLFZ2SaFjS4TyJR570.1840000.8942173870.5650.006680G0.2240-5.484Minor0.078896.9874-Apr0.686Alternative
910Kali UchisGotta Get Up - Interlude2KY0QUeRY3IPuJI1gyU9BJ510.6050000.4881132700.3810.087200D#0.0776-8.095Minor0.0498140.9414-Apr0.217Alternative
instance_idartist_nametrack_nametrack_idpopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalencegenre
499904991empty_fieldAh Yeah4Gyx7Nq6WySuaY6aE8aT8t490.69600.6323089330.4100.003070F0.1040-12.129Minor0.067771.9724-Apr0.3300Soul
499914992The DipSea Snake11XLXWcDdkKxvhvsk302QB350.44000.5161816000.4730.808000D0.1290-8.502Minor0.0343179.7063-Apr0.4980Soul
499924993Roy Ayers UbiquityLifeline0mtmrbRFsectINNpHdDkUQ430.18900.513-10.7470.069200D#0.0731-10.356Minor0.0362158.3884-Apr0.8250Soul
499934994The Gap BandOutstanding - Original 12" Mix7kB5KJlRtFWXtdHqhLmOvG640.37300.7923766400.5100.012000F#0.0319-10.649Major0.040298.8644-Apr0.8400Soul
499944995Amy WinehouseIntro / Stronger Than Me56z22nh9bb2tkWpvdPXlza390.14300.8672341110.4890.000148A0.0883-4.959Minor0.067691.4314-Apr0.6610Soul
499954996Penguin PrisonCalling Out1fpfYMT2RTc5W3Zluq5xVA410.11300.8572386270.6690.016800C0.1100-5.722Major0.0562122.7794-Apr0.9650Soul
499964997BeauvoisMars3DlgDXIYtnWtJKiB8bZQMv480.38100.6652437940.3690.914000C0.1080-16.111Major0.0483100.0154-Apr0.0335Soul
499974998empty_fieldSupaStar66EaKY9rBKpoPdxVeB78IP410.07110.7962523470.4660.000000C0.2560-9.386Minor0.304092.3014-Apr0.6530Soul
499984999SealDon't Cry7oCHyJtJiApwXeVVwJc9z3420.72700.2833779070.4210.000095D0.3300-8.112Major0.030592.5453-Apr0.1510Soul
499995000Z.Z. HillThat Ain't The Way You Make Love4Sd5tbNP4q8wS64abqwfkN390.41300.5762255600.5280.001680G#0.1240-9.030Minor0.037678.1174-Apr0.7160Soul